Showing posts with label biomedicine. Show all posts
Showing posts with label biomedicine. Show all posts

It's not your genes, stupid.


Imagine traveling back in time and meeting your caveman ancestor of 10,000 years ago. Imagine telling him about what life is like today: that, with the tap of a finger you turn darkness into light, a cold room into a warm one and a tube in the wall of your cave into a spring of hot and cold water. You tell him...
you can fly from one place to another, and watch any place on this Earth without ever leaving your cave. You tell him you never have to run after your food, or fear that you run out of it. Your ancestor will have a hard time believing you. In his world only his gods can do all that.
Then you tell him how some of your friends think his way of life is preferable for health, which is why you are visiting him because you want to see for yourself. Before I get to your ancestor's most likely answer, let's get on the same page with those friends of ours first.
You have probably heard them talk about the past 10,000 years having done nothing to our genetic make-up. In other words, your ancestor's DNA blueprint was the same as yours. Today this blueprint collides  with a space age environment in which we don't expend any energy to get our food, and the food we acquire delivers far more energy and far less nutrients than what had been the case during 99.9% of human evolution. 
According to this view, today's epidemics of obesity, diabetes, cardiovascular diseases and cancer are simply the collateral damage of this collision. This explanation is so persuasive that it is being parroted by every media type and talking head who can spell the word  'genetics'. I'm afraid it is not that simple. Here is why:
Remember when the 3 billion letters, or base-pairs, of the human genome had first been decoded at the beginning of this century. This decryption had been delivered with the promise of revolutionizing medicine. Aside from new therapies, the hottest items were prognostic and diagnostic tools, which, we were made to believe, would lay in front of each individual his biomedical future. And with this ability to predict would come the ability to prevent, specifically all those diseases which result from an unfavorable interaction between genes and environment.
Almost ten years later we are nowhere near this goal. OK, we have identified some associations between some genetic variants and the propensity to become obese or get a heart attack or diabetes. But these associations are far from strong and they hardly help us to improve risk prediction. Just this year, Vaarhorst and colleagues had investigated the ability of a genetic risk score to improve the risk prediction of conventional risk scores which are based on biomarkers, such as the ones used in the Framingham score. Less than 3% of the study participants would have been reclassified based on the genetic risk score [1].

In a study which was released just yesterday, genetic markers for the development of diabetes in asymptomatic people at high risk, did not improve conventional biomarker risk scoring at all [2]
Obviously we are not simply our genes. This is because genes do not make us sick or healthy. Genes make proteins. And on the way from gene to protein a lot of things happen on which genes do not have any influence. To express a gene, as biologists call it, that gene must first be transcribed on RNA and then translated from RNA into the final protein. Whether a gene is transcribed in the first place depends on whether it is being made accessible for this transcription process. Today we know at least two processes which can "silence" the expression of a gene, even though it is present in your DNA. These processes are called DNA methylation and histone modification. Simply imagine them as Mother Nature's way of keeping a gene under wraps.
That's a good thing if the protein product of the silenced gene would be detrimental to your health. It could well be the other way round, too. Anyway, these happenings have been called epigenetics. Epigenetic mechanisms enable cells to quickly match their protein production with changing environmental conditions. No need to wait for modifications of the genetic blueprint which takes many generations and a fair element of chance to materialize. The most astonishing discovery is that these epigenetic changes may become heritable, too. Which means, there is really no need to change the genetic code. 
I believe you get the picture now. While it is true that your ancestor's genetic code is indistinguishable from yours 10,000 years later, the way your body expresses this code in the form of proteins and hormones can differ in many ways. Which is why researchers are now as much excited about epigenetics as they used to be about genetics 10 years ago.
I don't want to be the party pooper, but whenever I see such excitement I'm reminded of how it has often evaporated after some further discoveries. Here I'm skeptical because of the picture, which we are beginning to see. Insulin, for example, is known to regulate the expression of many genes. At least in rats it has been shown that insulin's suppressive effect on gene expression in the liver, can be altered by short term fasting [3]. That means, relatively minor behavioral changes may affect the way our organism expresses its genetic code.   
Observations like these support the idea that we are not our genes, but what we make of them. In plain words: let's not hide behind the "it's-our-stone-age-genes" excuse, to explain why we are fat and lazy and ultimately chronically sick.
Now, back to your ancestor and his response to your friends' suggestions that his way of life is preferable for health. When you also tell him you live a lot longer than the 40 years he has on average, he'll tell you: You have got some nutcase friends over there. Let me live like a god first and then I'll worry about health later.
Maybe, we are not so different from our stone age ancestors after all. 







Lu, Y., Feskens, E., Boer, J., Imholz, S., Verschuren, W., Wijmenga, C., Vaarhorst, A., Slagboom, E., Müller, M., & Dollé, M. (2010). Exploring genetic determinants of plasma total cholesterol levels and their predictive value in a longitudinal study Atherosclerosis, 213 (1), 200-205 DOI: 10.1016/j.atherosclerosis.2010.08.053 

Zhang Y, Chen W, Li R, Li Y, Ge Y, & Chen G (2011). Insulin-regulated Srebp-1c and Pck1 mRNA expression in primary hepatocytes from zucker fatty but not lean rats is affected by feeding conditions. PloS one, 6 (6) PMID: 21731709

Am I shittin' you? Learn to be a skeptic!

Learn to be a skeptic!

Why you cannot believe what you read about medical studies.

In my last blog post I promised to tell you why you shouldn't trust any study results, particularly when you didn't read the study yourself. It has to do with the methods of biomedical research. To make my point, I'll take the gold standard research method, the double blinded randomized controlled trial, or RCT. 
Let's say we want to test a drug, which is supposed to lower blood pressure in those who suffer from hypertension. The researchers have decided to enroll, say, 100 "subjects". That's what we typically call the people who are kind enough to play guinea pig in our studies.   
The researchers will first do a randomization of subjects into one of two groups (very often it is more than one group, but to keep it simple we will assume just two groups). What we mean with randomization is that we randomly assign each subject to one of the two groups. One group - the intervention group - will receive the drug, the other group - the control group - won't. What they get instead is a sugar pill, a placebo. 
With the randomization we want to make sure that, at the start, or baseline, both groups are indistinguishable from each other with respect to their average vital parameters. For example, if we were to calculate the mean age, blood pressure and any other variable for each group, these mean values would be not different between groups. That's important, because we want to isolate the effect of the drug. We don't want to worry at the end whether the effect, or lack thereof, was maybe due to some significant difference between the groups at baseline. 
Once the randomization is done, we organize the trial in such a way that neither the "subjects" nor their physicians and nurses know whether they get the placebo or the active drug. Both sides are blind to what they get and give, which is why this set-up is called double-blinded. That's an important feature, because a researcher often goes into a study with a certain expectation of its outcome. Either that outcome supports his hypothesis, or it doesn't. To eliminate the risk of, more or less subconsciously, influencing the study towards a desired outcome, double-blinding is very effective tool.
Fast forward to the end of our trial. We have now all the data in hand to compare the two groups. After unblinding, the researchers will compare the two groups with each other. In our example, they will compare the average, or mean, of the blood pressure values of all the individuals for each group. If the intervention group's mean value is lower than that of the control group, then it is plausible to reject the null-hypothesis, that is to REJECT the idea that the drug is NOT as ineffective as the placebo (we are, of course, assuming here that the sugar pill didn't lower the blood pressure of the control group). 

There are statistical tools to determine whether the difference between the groups may just be a chance event, or whether chance is a very unlikely explanation. We can never rule out chance completely. Now, when we are confident that it is the drug and not pure chance, which has lowered the mean blood pressure in the intervention group, we write our paper to present it in one of the medical journals. 

If the subject is a little more sexy, than just lowering blood pressure, there will sure be some journalists who pick it up and report to their readers that, say, eating chocolate makes you slimmer. I'm not kidding. This headline very conveniently went through the media shortly before Easter this year [1]. Good for Hershey who are running it of course on their webpage. And in the media it reads like it did in the Irish Times: "Good news for chocoholics this Easter. Medical Matters: No need for guilt over all those Easter eggs."    


I'm not going to comment on the media geniuses, because it's their job to put an angle on every story, so that YOU find it interesting and read their stuff. But since I'm sure you'll follow these links, just let me warn you: the chocolate study was an observational study, not an RCT. And one thing we MUST NOT do with the results of observational studies is to confuse association with causation. Only when we conduct an RCT, where the intervention group eats chocolate and the control group doesn't, might we be able to determine whether there is a causal link. And for obvious reasons we can't blind the subjects, to whether they eat chocolate or not. But I'm digressing.
Back to our blood pressure study. When we compare the group averages, everything looks very convincing. And sure enough, as researchers we are happy with the results, and we are perfectly correct, when we conclude, that this medicine does its job. 
But will it do it for you? When you are hypertensive? You might be wrong if you say "Yes". And you will be wrong more often than we, as researchers, or your doctors care to admit. For one simple reason: The variability of effect within the group. You give 50 people the same drug, and I bet with you, and I'm not the betting type, that you'll have 50 different results. 
The mean value of the entire group glosses over these inter-individual differences. Let me give you an example from a study performed on 35 overweight men, who were studied in a supervised and carefully calculated 12-weeks exercise program, with the intention of reducing body weight. The mean weight loss was 3.7 kg. That was almost exactly the amount of weight loss which the researchers had expected from the additional energy expenditure of the exercise program. But when they looked at each individual, it became clear that the group mean doesn't tell you anything about how YOU would fare in that program. 
First of all, the standard deviation was 3.6 kg. Now, a standard deviation of 3.6 kg simply tells you that approximately two thirds of the participants experienced a weight loss anywhere between 3,7 kg (the mean) minus 3.6 kg and 3.7kg + 3.6 kg, that is between 0.1 kg and 7.3 kg! That's a lot of kilos. And what about the remaining one third of those participants? They are even further from these values. In this case the greatest loser went down by 14 kg, and the biggest "winner" gained almost 2 kg. A spread of 16 kilos!
Here is the graph which shows you the change on body weight and fat for each individual participant. Which one would you be?

This effect is what you do not see when you don't read the studies. And in most studies, it isn't made obvious either. 
Which is why, you shouldn't be surprised to learn that most major drugs are effective only in 25-60% of their users [2]. The same goes for weight loss drugs and interventions, for almost everything we study in biomedicine. 
That's not a problem for us in public health. Because a drug, which works in 60% of the patients, helps us reduce the burden of disease in our population. Public health is not interested whether you are one of the 60% or not. But you are. And that's why I believe not only medicine, but also prevention must be individualized.
 Which is why the GPS to chronic health, which I currently develop, is all about helping you find your individual path to your health objectives.
Why not have a look at it, and maybe even try it out? 

References